Towards Cost-Optimal Query Processing in the Cloud

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Abstract

Public cloud providers offer hundreds of heterogeneous hardware instances. For analytical query processing systems, this presents a major challenge: depending on the hardware configuration, performance and cost may differ by orders of magnitude. We propose a simple and intuitive model that takes the workload, hardware, and cost into account to determine the optimal instance configuration. We discuss how such a model-based approach can significantly reduce costs and also guide the evolution of cloud-native database systems to achieve our vision of cost-optimal query processing.